In today's computing environment, large amounts of data are generated, stored, and consumed. For example, users may access a social network to share photos, thoughts, and other information with friends. A company may store user login IDs and user resource access information within large data logs. Search websites may generate cookie IDs and search query information of users. It may be advantageous to organize and analyze such data based upon relationships between objects within the data. In one example, a corpus of data may represent millions of users and actions taken by the users. Associations between users and user actions may be used to determine a comprehensive understanding of the user (e.g., interests of the user, other users sharing the same interests, information relevant to the user, how the user may be affected by an epidemic, and/or millions of other observations about the user). In one example, a user may have browsed a car website using a cookie ID. Additionally, the user may have browsed a high-end expensive handbag website using a second cookie ID. The cookie IDs and user browsing actions may be correlated together to determine that the user may be interested in luxury cars. In this way, relevant information, such as targeted advertisements and luxury car reviews, may be provided to the user. In another example, public health information may be correlated together to identify target groups that may be affected by epidemic outbreaks. It may be appreciated that a variety of associations and inferences may be determined based upon processing large amounts of data, for example.